Well done! Finally, some systematic transcriptome profiling of the human brain on a large scale. If we are ever going to crack neurodevelopmental disorders, such datasets will be absolutely critical. Exon-level transcriptome and associated genotyping data, brain regions, gender differences, developmental trajectories—this manuscript has it all. However, this is only a start, a catalogue of molecular events that begs to be explored. We see the complexity contained within the dataset, and it is simply mind-boggling. How do we make sense out of all this? Which changes are characteristic of interneurons, and which trajectories are projection neuron derived? How are the changes related to maturation of layers or various diseases? The mining of this dataset is far from over. It will be interesting to see what a WGCNA type of analysis will uncover in this proverbial gold mine. We need new ideas, we need new bioinformatic tools to look at this.

In addition, based on the presented data, we need to form precise, testable hypotheses. And then will come the hardest part—we need to test these hypotheses, and this will be incredibly time consuming and very low throughput. From in-vitro systems, transgenic models, electrophysiology, neurochemistry to imaging, we should use everything at our disposal.

While the generation of this dataset is clearly long overdue, I also must note the enormous price tag that these experiments carry. Very few laboratories/groups in the world have resources to perform such studies, and such fishing expeditions/dataset-generation projects are poorly suited to regular NIH-funded mechanisms.

The Nature papers by Colantuoni et al. (2011) and Kang et al. (2011) are landmark studies, not only because of the wealth of data about the human brain transcriptome across the lifespan that they contain, but as a resource for other researchers to dip into or mine as they wish. Both papers represent the culmination of extensive research programs, and are based ultimately on the crucial, sensitive, and often unappreciated task of collecting a sufficient number of well-characterized brains (Deep-Soboslay et al., 2011). In turn (as noted by Karoly Mirnics in his comment), they also attest to the importance of having funding schemes which permit this kind of ambitious, long-term, large-scale—and expensive—research. The papers set a new gold standard for human brain studies in terms of size and scope. They also illustrate the renaissance of postmortem brain research, and provide confirmation (if any was needed) that human brain diseases need direct study of human brains—including normative analyses across the lifespan—if their genetic, neurodevelopmental, and molecular aspects are to be understood (Kleinman et al., 2011).

The papers will take time to digest fully. Early impressions reveal several findings of particular interest and relevance to schizophrenia.

1. It's striking just how dramatic are the transcriptional changes, even across a restricted fetal time period. Simple notions of a "second trimester" origin of a disorder need to become more nuanced.

2. The flow of alterations between fetal and infant life, and the infant-aging similarities and differences also speak to the dynamic temporal nature of the transcriptome, its regulation, refinement, and recapitulation.

3. The extent of regional (and sex) differences in gene expression and exon usage—and the interactions of these with development—found by Kang et al. are noteworthy, too, again attesting to the sheer complexity of the transcriptomic landscape.

4. The eQTL data in both studies emphasize the importance of cis variation in regulation of gene expression, especially for SNPs around transcriptional start sites; the P value of 10-78 (Fig. 3b in Colantuoni et al.) must be a record for a human brain study!

The data provide a much more detailed (albeit more complex) context within which to interpret deviations from the normal transcriptional profile in those with, or at risk of, schizophrenia. Notwithstanding the huge number of data in these papers, many questions remain unanswered. There is a relative gap across mid-childhood—for obvious reasons—which later studies can fill in (c.f. the accompanying Nature editorial on the need to collect more brains from children). Future studies will also hopefully move to sequencing methods, extend to other brain regions, and address the daunting task of protein-based equivalent studies. Finally, as the authors of both papers note, the current data are from tissue homogenates, and so cannot reveal differential changes in one cell type from another. We can expect these last differences to be as complicated and fascinating as the temporal and regional profiles reported here.

A key issue for researchers interested in the neurobiology of genes involved in schizophrenia is how deep to dig when investigating the expression of a gene (as one aspect of its function or pathology) before deciding enough is enough. The data in these papers indicate that the answer is probably "very deep." Stretching the metaphor, the data also highlight that there may need to be several digs, across time and space, in looking for different kinds of molecular treasure.

I am interested in the database on significant associations between SNPs and gene expression levels in the prefrontal cortex (PFC) because we might be able to understand how genetic variance associated with schizophrenia contributes to PFC dysfunction in schizophrenia. The database will allow us to identify functional genes whose expression levels in the PFC are controlled by schizophrenia-associated SNPs. If they are expressed and have specific roles in the mature PFC, we can assess their significance in the pathophysiology by evaluating expression changes in schizophrenia using postmortem brains. It is also possible that some of these genes have already been reported to exhibit altered expression levels in the PFC of schizophrenia subjects, contributing to specific aspects of PFC dysfunctions. Therefore, this database appears to be important for further understanding of the pathogenetic and pathophysiological mechanisms of schizophrenia and the development of efficient treatments for PFC dysfunction in schizophrenia.

However, to my disappointment, the access is restricted to NIH-funded principal investigators and not available to researchers across the world. I hope this part of the study also becomes open to researchers in nonprofit academic institutes worldwide.

These two new papers show the spatial and temporal regulation of gene expression in the human brain across various ages. Although it is not novel to observe various patterns of gene expression during human brain development, systematic bioinformatics approaches using such enormous sample sizes will lead us to a new level of understanding the complexity of the transcriptome during development.

Both groups showed that age is a very strong contributor to global differences in gene expression compared to other variables such as sex, ethnicity, and inter-individual variation. Thus, transcriptional differences and changes are most pronounced during early development, gradually slowing through infancy, adolescence, and into adulthood—each stage having a clear transcriptional profile. Kang et al. further showed that gene expression is also spatially regulated. Furthermore, they found many co-expressed gene groups that were spatially and temporally regulated. They also reported sex-biased gene expression.

Our group, like many other laboratories, is trying to approach molecular mechanism(s) underlying schizophrenia by using patient-derived cells, especially induced pluripotent stem cells (Dolmetsch and Geschwind, 2011) and immature neurons obtained from nasal biopsy (Sawa and Cascella, 2009). The challenge in this approach has been the shortage of information on gene expression patterns during the neurodevelopmental trajectory. In this sense, these two outstanding papers provide all of us with useful information. If any future studies can address the spatial and temporal regulation of gene expression in each “specific” type of brain cell, this will be of further help to the field. Laser-captured microdissection could be a useful tool to obtain enriched populations of different cell types from tissue (Goswami et al., 2010; Tajinda et al., 2010). Such encyclopedia-type efforts may also be applied to reveal the epigenetic landscape of the brain in the future (Cheung et al., 2010).